EDA on Coffee Quality Dataset
¶

image.png

Identification of attributes and implementing the DataSet¶

In [6]:
#Importing necessary libraries to perform EDA
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
In [7]:
#To read/open csv file
df=pd.read_csv("df_Coffee.csv")
print("First/Top 2 records>>>")
#Top 2 records serially
df.head(2)

First/Top 2 records>>>
Out[7]:
Unnamed: 0 ID Country of Origin Farm Name Lot Number Mill ICO Number Company Altitude Region ... Total Cup Points Moisture Percentage Category One Defects Quakers Color Category Two Defects Expiration Certification Body Certification Address Certification Contact
0 0 0 Colombia Finca El Paraiso CQU2022015 Finca El Paraiso NaN Coffee Quality Union 1700-1930 Piendamo,Cauca ... 89.33 11.8 0 0 green 3 September 21st, 2023 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
1 1 1 Taiwan Royal Bean Geisha Estate The 2022 Pacific Rim Coffee Summit,T037 Royal Bean Geisha Estate NaN Taiwan Coffee Laboratory 1200 Chiayi ... 87.58 10.5 0 0 blue-green 0 November 15th, 2023 Taiwan Coffee Laboratory 台灣咖啡研究室 QAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd... Lin, Jen-An Neil 林仁安 - 886-289116612

2 rows × 41 columns

In [9]:
#Breif information of all attributes
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 207 entries, 0 to 206
Data columns (total 41 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   Unnamed: 0             207 non-null    int64  
 1   ID                     207 non-null    int64  
 2   Country of Origin      207 non-null    object 
 3   Farm Name              205 non-null    object 
 4   Lot Number             206 non-null    object 
 5   Mill                   204 non-null    object 
 6   ICO Number             75 non-null     object 
 7   Company                207 non-null    object 
 8   Altitude               206 non-null    object 
 9   Region                 205 non-null    object 
 10  Producer               206 non-null    object 
 11  Number of Bags         207 non-null    int64  
 12  Bag Weight             207 non-null    object 
 13  In-Country Partner     207 non-null    object 
 14  Harvest Year           207 non-null    object 
 15  Grading Date           207 non-null    object 
 16  Owner                  207 non-null    object 
 17  Variety                201 non-null    object 
 18  Status                 207 non-null    object 
 19  Processing Method      202 non-null    object 
 20  Aroma                  207 non-null    float64
 21  Flavor                 207 non-null    float64
 22  Aftertaste             207 non-null    float64
 23  Acidity                207 non-null    float64
 24  Body                   207 non-null    float64
 25  Balance                207 non-null    float64
 26  Uniformity             207 non-null    float64
 27  Clean Cup              207 non-null    float64
 28  Sweetness              207 non-null    float64
 29  Overall                207 non-null    float64
 30  Defects                207 non-null    float64
 31  Total Cup Points       207 non-null    float64
 32  Moisture Percentage    207 non-null    float64
 33  Category One Defects   207 non-null    int64  
 34  Quakers                207 non-null    int64  
 35  Color                  207 non-null    object 
 36  Category Two Defects   207 non-null    int64  
 37  Expiration             207 non-null    object 
 38  Certification Body     207 non-null    object 
 39  Certification Address  207 non-null    object 
 40  Certification Contact  207 non-null    object 
dtypes: float64(13), int64(6), object(22)
memory usage: 66.4+ KB
In [10]:
#Statistical infromation of the given coffee data set attribute
df.describe()
Out[10]:
Unnamed: 0 ID Number of Bags Aroma Flavor Aftertaste Acidity Body Balance Uniformity Clean Cup Sweetness Overall Defects Total Cup Points Moisture Percentage Category One Defects Quakers Category Two Defects
count 207.000000 207.000000 207.000000 207.000000 207.000000 207.000000 207.00000 207.000000 207.000000 207.000000 207.0 207.0 207.000000 207.0 207.000000 207.000000 207.000000 207.000000 207.000000
mean 103.000000 103.000000 155.449275 7.721063 7.744734 7.599758 7.69029 7.640918 7.644058 9.990338 10.0 10.0 7.676812 0.0 83.706570 10.735266 0.135266 0.690821 2.251208
std 59.899917 59.899917 244.484868 0.287626 0.279613 0.275911 0.25951 0.233499 0.256299 0.103306 0.0 0.0 0.306359 0.0 1.730417 1.247468 0.592070 1.686918 2.950183
min 0.000000 0.000000 1.000000 6.500000 6.750000 6.670000 6.83000 6.830000 6.670000 8.670000 10.0 10.0 6.670000 0.0 78.000000 0.000000 0.000000 0.000000 0.000000
25% 51.500000 51.500000 1.000000 7.580000 7.580000 7.420000 7.50000 7.500000 7.500000 10.000000 10.0 10.0 7.500000 0.0 82.580000 10.100000 0.000000 0.000000 0.000000
50% 103.000000 103.000000 14.000000 7.670000 7.750000 7.580000 7.67000 7.670000 7.670000 10.000000 10.0 10.0 7.670000 0.0 83.750000 10.800000 0.000000 0.000000 1.000000
75% 154.500000 154.500000 275.000000 7.920000 7.920000 7.750000 7.87500 7.750000 7.790000 10.000000 10.0 10.0 7.920000 0.0 84.830000 11.500000 0.000000 1.000000 3.000000
max 206.000000 206.000000 2240.000000 8.580000 8.500000 8.420000 8.58000 8.250000 8.420000 10.000000 10.0 10.0 8.580000 0.0 89.330000 13.500000 5.000000 12.000000 16.000000
In [11]:
# To count the total rows and columns present in the data set
df.shape
Out[11]:
(207, 41)
In [12]:
# Column names 
df.columns
Out[12]:
Index(['Unnamed: 0', 'ID', 'Country of Origin', 'Farm Name', 'Lot Number',
       'Mill', 'ICO Number', 'Company', 'Altitude', 'Region', 'Producer',
       'Number of Bags', 'Bag Weight', 'In-Country Partner', 'Harvest Year',
       'Grading Date', 'Owner', 'Variety', 'Status', 'Processing Method',
       'Aroma', 'Flavor', 'Aftertaste', 'Acidity', 'Body', 'Balance',
       'Uniformity', 'Clean Cup', 'Sweetness', 'Overall', 'Defects',
       'Total Cup Points', 'Moisture Percentage', 'Category One Defects',
       'Quakers', 'Color', 'Category Two Defects', 'Expiration',
       'Certification Body', 'Certification Address', 'Certification Contact'],
      dtype='object')
In [13]:
# Determining the start and the stop index
df.index
Out[13]:
RangeIndex(start=0, stop=207, step=1)

Observation¶

  • In this block,there were basic syntax to get a brief overview of the dataset.Here are few conclusions drawn: -There are 207 rows and 40 columns. -Also drawn the statistical inference. -The index starts from 0 and goes till 207.

DataCleaning¶

image.png

In [42]:
#Removing Unwanted column
df.drop('Unnamed: 0',axis=1)
Out[42]:
ID Country of Origin Farm Name Lot Number Mill ICO Number Company Altitude Region Producer ... Total Cup Points Moisture Percentage Category One Defects Quakers Color Category Two Defects Expiration Certification Body Certification Address Certification Contact
0 0 Colombia Finca El Paraiso CQU2022015 Finca El Paraiso NaN Coffee Quality Union 1700-1930 Piendamo,Cauca Diego Samuel Bermudez ... 89.33 11.8 0 0 green 3 September 21st, 2023 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
1 1 Taiwan Royal Bean Geisha Estate The 2022 Pacific Rim Coffee Summit,T037 Royal Bean Geisha Estate NaN Taiwan Coffee Laboratory 1200 Chiayi 曾福森 ... 87.58 10.5 0 0 blue-green 0 November 15th, 2023 Taiwan Coffee Laboratory 台灣咖啡研究室 QAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd... Lin, Jen-An Neil 林仁安 - 886-289116612
2 2 Laos OKLAO coffee farms The 2022 Pacific Rim Coffee Summit,LA01 oklao coffee processing plant NaN Taiwan Coffee Laboratory 1300 Laos Borofen Plateau WU TAO CHI ... 87.42 10.4 0 0 yellowish 2 November 15th, 2023 Taiwan Coffee Laboratory 台灣咖啡研究室 QAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd... Lin, Jen-An Neil 林仁安 - 886-289116612
3 3 Costa Rica La Cumbre CQU2022017 La Montana Tarrazu MIll NaN Coffee Quality Union 1900 Los Santos,Tarrazu Santa Maria de Dota ... 87.17 11.8 0 0 green 0 September 21st, 2023 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
4 4 Colombia Finca Santuario CQU2023002 Finca Santuario NaN Coffee Quality Union 1850-2100 Popayan,Cauca Camilo Merizalde ... 87.08 11.6 0 2 yellow-green 2 March 5th, 2024 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
202 202 Brazil Fazenda Conquista 019/22 Dry Mill NaN Ipanema Coffees 950 Sul de Minas Ipanema Coffees ... 80.08 11.4 0 0 green 4 February 2nd, 2024 Brazil Specialty Coffee Association Rua Gaspar Batista Paiva, 416 – Santa Luiza Va... Chris Allen - 55 35 3212-4705
203 203 Nicaragua Finca San Felipe 017-053-0155 Beneficio Atlantic Sébaco 017-053-0155 Exportadora Atlantic S.A 1200 Matagalpa Exportadora Atlantic S.A. ... 80.00 10.4 0 2 green 12 March 2nd, 2024 Asociación de Cafés Especiales de Nicaragua Del Hotel Seminole 2 C al lago, 1 C arriba. Maria Ines Benavidez Toval - 011-(505)-8396 4717
204 204 Laos - 105/3/VL7285-005 DRY MILL 105/3/VL7285-005 Marubeni Corporation 1300 Bolaven Plateau LAO MINH TIEN COFFEE SOLE CO.,LTD ... 79.67 11.6 0 9 green 11 November 11th, 2023 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
205 205 El Salvador Rosario de Maria II, Area de La Pila 0423A01 Optimum Coffee, San Salvador, El Salvador NaN Aprentium Enterprises LLC 1200 Volcan de San Vicente, La Paz, El Salvador Roselia Yglesias ... 78.08 11.0 0 12 bluish-green 13 March 7th, 2024 Salvadoran Coffee Council Final 1a. Av. Norte y 13 Calle Pte., dentro de... Tomas Bonilla - (503) 2505-6600
206 206 Brazil Walter Matter 1058 y 1059 Beneficio humedo/seco 002/1208/1016 Descafeinadores Mexicano SA. de CV 850-1100 Minas Gerais Walter Matter ... 78.00 11.3 0 0 green 1 November 18th, 2023 Centro Agroecológico del Café A.C. Instituto de Ecología A. C., Campus III Edific... Stephany Escamilla Femat - Tel. +52 228 842180...

207 rows × 40 columns

In [46]:
#To rename a column from bag_weight to bag_weight\kg
df1=df.rename(
    columns={
        'bag_weight': 'bag_weight\kg'
    })
In [46]:
# Top 5 rows from df1 dataSet
df1.head()
Out[46]:
Unnamed: 0 ID Country of Origin Farm Name Lot Number Mill ICO Number Company Altitude Region ... Total Cup Points Moisture Percentage Category One Defects Quakers Color Category Two Defects Expiration Certification Body Certification Address Certification Contact
0 0 0 Colombia Finca El Paraiso CQU2022015 Finca El Paraiso NaN Coffee Quality Union 1700-1930 Piendamo,Cauca ... 89.33 11.8 0 0 green 3 September 21st, 2023 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
1 1 1 Taiwan Royal Bean Geisha Estate The 2022 Pacific Rim Coffee Summit,T037 Royal Bean Geisha Estate NaN Taiwan Coffee Laboratory 1200 Chiayi ... 87.58 10.5 0 0 blue-green 0 November 15th, 2023 Taiwan Coffee Laboratory 台灣咖啡研究室 QAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd... Lin, Jen-An Neil 林仁安 - 886-289116612
2 2 2 Laos OKLAO coffee farms The 2022 Pacific Rim Coffee Summit,LA01 oklao coffee processing plant NaN Taiwan Coffee Laboratory 1300 Laos Borofen Plateau ... 87.42 10.4 0 0 yellowish 2 November 15th, 2023 Taiwan Coffee Laboratory 台灣咖啡研究室 QAHWAH CO., LTD 4F, No. 225, Sec. 3, Beixin Rd... Lin, Jen-An Neil 林仁安 - 886-289116612
3 3 3 Costa Rica La Cumbre CQU2022017 La Montana Tarrazu MIll NaN Coffee Quality Union 1900 Los Santos,Tarrazu ... 87.17 11.8 0 0 green 0 September 21st, 2023 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901
4 4 4 Colombia Finca Santuario CQU2023002 Finca Santuario NaN Coffee Quality Union 1850-2100 Popayan,Cauca ... 87.08 11.6 0 2 yellow-green 2 March 5th, 2024 Japan Coffee Exchange 〒413-0002 静岡県熱海市伊豆山１１７３−５８ 1173-58 Izusan, Ata... 松澤　宏樹　Koju Matsuzawa - +81(0)9085642901

5 rows × 41 columns

In [15]:
dfn=df1.select_dtypes(exclude='object')
In [16]:
# To check whether the given rows have duplicate values
dfn.duplicated()
Out[16]:
0      False
1      False
2      False
3      False
4      False
       ...  
202    False
203    False
204    False
205    False
206    False
Length: 207, dtype: bool
In [17]:
dfn.duplicated().sum()
Out[17]:
0
In [18]:
# To check whether the given rows have null values
dfn.isnull()
Out[18]:
Unnamed: 0 ID Number of Bags Aroma Flavor Aftertaste Acidity Body Balance Uniformity Clean Cup Sweetness Overall Defects Total Cup Points Moisture Percentage Category One Defects Quakers Category Two Defects
0 False False False False False False False False False False False False False False False False False False False
1 False False False False False False False False False False False False False False False False False False False
2 False False False False False False False False False False False False False False False False False False False
3 False False False False False False False False False False False False False False False False False False False
4 False False False False False False False False False False False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
202 False False False False False False False False False False False False False False False False False False False
203 False False False False False False False False False False False False False False False False False False False
204 False False False False False False False False False False False False False False False False False False False
205 False False False False False False False False False False False False False False False False False False False
206 False False False False False False False False False False False False False False False False False False False

207 rows × 19 columns

In [19]:
dfn.isnull().sum()
Out[19]:
Unnamed: 0              0
ID                      0
Number of Bags          0
Aroma                   0
Flavor                  0
Aftertaste              0
Acidity                 0
Body                    0
Balance                 0
Uniformity              0
Clean Cup               0
Sweetness               0
Overall                 0
Defects                 0
Total Cup Points        0
Moisture Percentage     0
Category One Defects    0
Quakers                 0
Category Two Defects    0
dtype: int64

Observations¶

-There existed a column with no significance 'Unnamed: 0',that has been dropped.
-All the values are checked whether there are null present in the dataset,but none of them has null values.
-All the values are checked whether there are duplicated present in the dataset,but none of them has duplicated values.

EDA on coffee data set¶

In [20]:
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
sns.set()
In [48]:
cat_list = df1.select_dtypes(include=["object"]).columns.tolist()
for col in cat_list: 
    plt.figure(figsize=(8,5))
    top10 = df1[col].value_counts()[:10]
    top10.plot(kind='bar')
    plt.title("Top 10 " + col)
    plt.grid(visible=False)
    plt.show()
In [21]:
# company wise count
df1['Company'].value_counts()#Return a Series containing counts of unique rows in the DataFrame.
Out[21]:
Taiwan Coffee Laboratory        51
Taiwu Coffee Cooperative        25
Coffee Quality Union            15
Doi Tung Development Project     7
Peter Schoenfeld, S.A.           6
                                ..
Taylor Winch Coffee Ltd          1
ECOM COLOMBIA                    1
Exportadora Café California      1
Coffee Quality Institute         1
marubeni                         1
Name: Company, Length: 72, dtype: int64
In [65]:
# Variety wise count
df1['Variety'].value_counts()#Return a Series containing counts of unique rows in the DataFrame.
Out[65]:
Caturra                                                         27
Gesha                                                           27
Typica                                                          23
Bourbon                                                         17
Catuai                                                          12
unknown                                                         12
Catimor                                                         11
Ethiopian Heirlooms                                              9
SL34                                                             8
Mundo Novo                                                       4
SL14                                                             3
Yellow Bourbon                                                   3
SHG                                                              3
Java                                                             3
Maragogype                                                       2
Parainema                                                        2
Pacamara                                                         2
Sarchimor                                                        2
SL28                                                             2
Santander                                                        1
Typica Gesha                                                     1
Catucai                                                          1
Yellow Catuai                                                    1
SL28,SL34,Ruiru11                                                1
Caturra-Catuai                                                   1
Typica Bourbon Caturra Catimor                                   1
Caturra,Colombia,Castillo                                        1
Castillo,Caturra,Bourbon                                         1
unknow                                                           1
Bourbon, Catimor, Caturra, Typica                                1
Pacas                                                            1
Gayo                                                             1
Castillo                                                         1
Lempira                                                          1
Red Bourbon,Caturra                                              1
MARSELLESA, CATUAI, CATURRA & MARSELLESA, ANACAFE 14, CATUAI     1
Typica + SL34                                                    1
Catimor,Catuai,Caturra,Bourbon                                   1
Bourbon Sidra                                                    1
BOURBON, CATURRA Y CATIMOR                                       1
Jember,TIM-TIM,Ateng                                             1
Castillo and Colombia blend                                      1
Catrenic                                                         1
Castillo Paraguaycito                                            1
Wolishalo,Kurume,Dega                                            1
Sl34+Gesha                                                       1
Red Bourbon                                                      1
Catuai and Mundo Novo                                            1
Name: Variety, dtype: int64
In [22]:
# color wise count
df1['Color'].value_counts()#Return a Series containing counts of unique rows in the DataFrame.
Out[22]:
green            101
greenish          36
bluish-green      21
blue-green        12
yellow-green      10
brownish           9
pale yellow        6
yellow green       5
yellowish          4
yellow- green      1
browish-green      1
yello-green        1
Name: Color, dtype: int64
In [56]:
df1.groupby('Country of Origin')['Country of Origin'].agg('count')

#A groupby operation involves some combination of splitting the
# object, applying a function, and combining the results.
Out[56]:
Country of Origin
Brazil                          10
Colombia                        19
Costa Rica                       8
El Salvador                      7
Ethiopia                        11
Guatemala                       21
Honduras                        13
Indonesia                        3
Kenya                            2
Laos                             3
Madagascar                       1
Mexico                           4
Myanmar                          1
Nicaragua                        7
Panama                           2
Peru                             4
Taiwan                          61
Tanzania, United Republic Of     6
Thailand                        12
Uganda                           3
United States (Hawaii)           5
Vietnam                          4
Name: Country of Origin, dtype: int64
In [35]:
#Creating a GRAPH to analyze AROMA distribution
ax = df1['Aroma'].plot(kind='hist',bins=20,title= 'Aroma Score')
ax.set_xlabel("Aroma Score")
plt.show()

observation¶

- Aroma score,i.e,between 7.5 to 8.0, has the highest frequency.
In [33]:
#Creating a GRAPH to analyze Flavour distribution
ax = df1['Flavor'].plot(kind='hist', bins=20, title= 'Flavor Score')
ax.set_xlabel("Flavor Score")
plt.show()

Observation¶

-  Flavor score,i.e,between 7.75 to 8.0, has the highest frequency.
In [34]:
#Creating a GRAPH to analyze After Taste distribution
ax = df1['Aftertaste'].plot(kind='hist', bins=20, title= 'Aftertaste Score')
ax.set_xlabel("Aftertaste Score")
plt.show()

Observation¶

- Aftertaste score,i.e,between 7.50 to 7.75, has the highest frequency.
In [36]:
#Creating a GRAPH to analyze Acidity Analysis distribution
ax = df1['Acidity'].plot(kind='hist', bins=20, title= 'Acidity Score')
ax.set_xlabel("Acidity Score")
plt.show()

Observation¶

- Acidity score,i.e,between 7.50 to 8.0, has the highest frequency.
In [37]:
#Creating a GRAPH to analyze Sweetness Analysis distribution
ax = df1['Sweetness'].plot(kind='hist', bins=20, title= 'Sweetness Score')
ax.set_xlabel("Sweetness Score")
plt.show()

Observation¶

- Sweetness score is 10 for frequency 200+,this signifies all the flavours/coffee has sweetness score 10.
In [38]:
#Creating a GRAPH to analyze Overall Analysis distribution
ax = df1['Overall'].plot(kind='hist', bins=20, title= 'Overall Score')
ax.set_xlabel("Overall Score" )
plt.show()

Observation¶

- Overall score,i.e,between 7.50 to 8.0, has the highest frequency.
In [39]:
#Creating a GRAPH to analyze Defects Analysis distribution
ax = df1['Defects'].plot(kind='hist', bins=20, title= 'Defects Score')
ax.set_xlabel("Defects Score")
plt.show()

Observation¶

- Defects score is 0.0 for 200+ frequencies, this  signifies all the rows have no defects.
In [40]:
#Creating a GRAPH to analyze Total Cup Points distribution
ax = df1['Total Cup Points'].plot(kind='hist', bins=20, title= 'Total Cup Points Score')
ax.set_xlabel("Total Cup Points Score")
plt.show()

Observation¶

- Total cup points score,i.e,from 82 to 86, has the highest frequency.
In [53]:
# Plotting a scatter plot of flavor and aftertaste scores
plt.figure(figsize=(8, 6))

# Plotting the scatter plot
plt.scatter(df1['Flavor'], df1['Aftertaste'])

# Adding labels and title
plt.xlabel('Flavor')
plt.ylabel('Aftertaste')
plt.title('Scatter Plot of Flavor and Aftertaste Scores')

# Displaying the plot
plt.show()
In [47]:
# Determining the countries Maximum and the Minimum total cup score
mean_country_cupscore = df1.groupby('Country of Origin')['Total Cup Points'].mean().reset_index().head(10)
mean_country_cupscore.sort_values('Total Cup Points', ascending=False)
Out[47]:
Country of Origin Total Cup Points
4 Ethiopia 84.960909
5 Guatemala 84.301429
1 Colombia 83.877368
2 Costa Rica 83.740000
8 Kenya 83.710000
7 Indonesia 83.693333
9 Laos 83.390000
6 Honduras 83.282308
0 Brazil 81.883000
3 El Salvador 81.532857

Observations¶

-Ethiopia has the highest total cup  score with mean score 84.960909.
-El Salvador has the lowest total cup  score with mean score 81.532857.